\documentclass{article}

\usepackage{arxiv}

\usepackage[utf8]{inputenc} % allow utf-8 input
\usepackage[T1]{fontenc}    % use 8-bit T1 fonts
\usepackage{lmodern}        % https://github.com/rstudio/rticles/issues/343
\usepackage{hyperref}       % hyperlinks
\usepackage{url}            % simple URL typesetting
\usepackage{booktabs}       % professional-quality tables
\usepackage{amsfonts}       % blackboard math symbols
\usepackage{nicefrac}       % compact symbols for 1/2, etc.
\usepackage{microtype}      % microtypography
\usepackage{graphicx}

\title{The Latent Influence of Ideology on Partisan Media Consumption}

\author{
    Matthew E. Dardet
   \\
    Department of Government \\
    Harvard University \\
  Cambridge, MA 02138 \\
  \texttt{\href{mailto:matthewdardet@fas.harvard.edu}{\nolinkurl{matthewdardet@fas.harvard.edu}}} \\
   \And
    Ben TerMaat
   \\
    Department of Government \\
    Harvard University \\
  Cambridge, MA 02138 \\
  \texttt{\href{mailto:btermaat@g.harvard.edu}{\nolinkurl{btermaat@g.harvard.edu}}} \\
  }



% Pandoc citation processing

\usepackage{float} \usepackage{graphicx} \usepackage{longtable} \usepackage{rotating} \usepackage{hhline} \usepackage{dcolumn}
\usepackage{booktabs}
\usepackage{longtable}
\usepackage{array}
\usepackage{multirow}
\usepackage{wrapfig}
\usepackage{float}
\usepackage{colortbl}
\usepackage{pdflscape}
\usepackage{tabu}
\usepackage{threeparttable}
\usepackage{threeparttablex}
\usepackage[normalem]{ulem}
\usepackage{makecell}
\usepackage{xcolor}


\begin{document}
\maketitle

\def\tightlist{}


\begin{abstract}
We introduce a new, simple variance-parametric statistic---mean absolute
uncertainty deviation (MAUD) and its scaled cousin, standardized mean
absolute uncertainty deviation (SMAUD) uncertainty---for detecting
initial evidence of model dependence. Using these descriptive
statistics, generalized information matrix (GIM) tests, and
leave-one-covariate-out (LOCO) inference, we reanalyze high-quality data
and research designs on media diets from Guess (2021) and Tyler,
Grimmer, \& Iyengar (2021) and show that models of Americans' media
consumption that incorporate measures of respondent
\textit{ideological proclivities} can be less model dependent, explain
more variance than their ideology-less counterparts, and shrink
kernel-based overlap coefficients over ideologically stratified
distributions. Finally, we find that using URL section names as proxies
for classification of news articles explain slightly less variance in
partisan media consumption yet exhibit less model dependence compared to
using a full logistic regression classifier, suggesting that the two
methdologies may not be measuring the exact same quantity.
\end{abstract}

\keywords{
    media diets
   \and
    motivated reasoning
   \and
    political polarization
   \and
    machine learning
  }

\hypertarget{introduction}{%
\section{Introduction}\label{introduction}}

\hypertarget{research-design-and-data}{%
\section{Research Design and Data}\label{research-design-and-data}}

\hypertarget{measuring-media-consumption-and-computing-partisan-slants}{%
\subsection{Measuring Media Consumption and Computing Partisan
Slants}\label{measuring-media-consumption-and-computing-partisan-slants}}

Following Guess (2021), we use domain alignment scores from Bakshy et
al.~(2015) and a logistic regression-based news classifier to determine
the average media consumption of each of the respondents who agreed to
allow the Wakoopa search bar to track their web-browsing histories.

\begin{center}\includegraphics[width=1\linewidth,]{output/figures/figure_1} \end{center}

{[}TODO: Overlap coefficients. Guess (2021) and Inman and Bradley
(1989){]}

\begin{table}[H]

\caption{\label{tab:unnamed-chunk-3}\textbf{Overlap Coefficients for Americans' Partisan Media Diets}}
\centering
\begin{tabular}[t]{lcccccc}
\toprule
  & 2015 & 2015 (no portals) & 2016 & 2016 (no portals) & $\Delta_{P}$ & $\Delta_{NP}$\\
\midrule
Dem-Rep & 0.629 & 0.596 & 0.478 & 0.429 & -0.150 & -0.167\\
Dem-Ind & 0.825 & 0.770 & 0.731 & 0.679 & -0.094 & -0.090\\
Rep-Ind & 0.790 & 0.799 & 0.741 & 0.720 & -0.048 & -0.078\\
\bottomrule
\end{tabular}
\end{table}

{[}TODO: we find, after computing overlap coefficients, that the overlap
in partisan media diets has considerably considerately in 2016 compared
to 2015, as evidenced by the negative coefficients on the change in
portal and nonportal diet alignments from 2015 to 2016. It is unclear
whether this is a sign of a significant change in news consumption
between 2015 and 2016 or a statistical artefact induced by changes in
the survey and observational samples between the two years. We will
return to this question later in the paper\ldots{]}

\begin{table}[H] \centering 
  \caption{\textbf{Determinants of Media Diet Slant}} 
  \label{} 
\begin{tabular}{@{\extracolsep{0pt}}lD{.}{.}{-3} D{.}{.}{-3} } 
\\[-1.8ex]\hline \\[-1.8ex] 
\\[-1.8ex] & \multicolumn{2}{c}{Average media diet slant (news/politics only)} \\ 
 & \multicolumn{1}{c}{\emph{2015}} & \multicolumn{1}{c}{\emph{2016}} \\ 
\\[-1.8ex] & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)}\\ 
\hline \\[-1.8ex] 
 Age: 30-44 & 0.136^{**} & 0.062 \\ 
  & (0.034) & (0.035) \\ 
  Age: 45-59 & 0.205^{**} & 0.123^{**} \\ 
  & (0.037) & (0.029) \\ 
  Age: 60+ & 0.242^{**} & 0.203^{**} \\ 
  & (0.051) & (0.028) \\ 
  Race: Black & 0.010 & -0.047 \\ 
  & (0.047) & (0.046) \\ 
  Race: Hispanic & -0.017 & 0.110 \\ 
  & (0.054) & (0.066) \\ 
  Race: White & -0.038 & 0.021 \\ 
  & (0.037) & (0.040) \\ 
  Female & -0.022 & -0.024 \\ 
  & (0.030) & (0.021) \\ 
  Income level & -0.002 & 0.005 \\ 
  & (0.005) & (0.003) \\ 
  High school & 0.019 & 0.079 \\ 
  & (0.071) & (0.073) \\ 
  Some college & -0.048 & 0.055 \\ 
  & (0.062) & (0.071) \\ 
  College graduate & -0.026 & 0.017 \\ 
  & (0.068) & (0.071) \\ 
  Postgraduate & -0.117 & -0.006 \\ 
  & (0.076) & (0.072) \\ 
  Democrat & -0.258^{**} & -0.189^{**} \\ 
  & (0.060) & (0.044) \\ 
  Independent & -0.074 & -0.017 \\ 
  & (0.062) & (0.046) \\ 
  Republican & 0.040 & 0.127^{**} \\ 
  & (0.067) & (0.048) \\ 
  Constant & -0.093 & -0.283^{**} \\ 
  & (0.075) & (0.081) \\ 
 N & \multicolumn{1}{c}{861} & \multicolumn{1}{c}{1,903} \\ 
Adjusted R$^{2}$ & \multicolumn{1}{c}{0.192} & \multicolumn{1}{c}{0.238} \\ 
\hline \\[-1.8ex] 
\multicolumn{3}{l}{$^{*}$p $<$ .05; $^{**}$p $<$ .01; $^{***}$p $<$ [.***]} \\ 
\multicolumn{3}{l}{OLS regressions with HC2 robust standard errors in} \\ 
\multicolumn{3}{l}{parentheses; YouGov survey data with weights applied.} \\ 
\end{tabular} 
\end{table}

\hypertarget{model-dependence-in-existing-models-of-partisan-media-consumption}{%
\subsection{Model Dependence in Existing Models of Partisan Media
Consumption}\label{model-dependence-in-existing-models-of-partisan-media-consumption}}

{[}TODO: introduce MAUD or SMAUD{]} {[}TODO: summary of GIM Test{]}

\begin{table}[H]

\caption{\label{tab:unnamed-chunk-7}\textbf{Evidence for Model Dependence in Main Consumption Determinants Models}}
\centering
\begin{tabular}[t]{lccccc}
\toprule
  & MAUD & SMAUD & (GIM) Rule of Thumb & Test Statistic & \textit{p}-value\\
\midrule
2015 & 0.008 & 0.167 & 1.865 & 1479.103 & 0.032\\
2016 & 0.018 & 0.349 & 1.865 & 1495.154 & 0.032\\
\bottomrule
\end{tabular}
\end{table}

\hypertarget{considering-ideology-reduces-model-dependence-and-shrinks-overlap-coefficients}{%
\subsection{Considering Ideology Reduces Model Dependence and Shrinks
Overlap
Coefficients}\label{considering-ideology-reduces-model-dependence-and-shrinks-overlap-coefficients}}

{[}TODO: history of partisan newspapers has largely been replaced {]}

\begin{table}[H] \centering 
  \caption{\textbf{Determinants of Media Diet Slant (Including Ideology)}} 
  \label{} 
\small 
\begin{tabular}{@{\extracolsep{0pt}}lD{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} } 
\\[-1.8ex]\hline \\[-1.8ex] 
\\[-1.8ex] & \multicolumn{6}{c}{Average media diet slant (news/politics only)} \\ 
 & \multicolumn{1}{c}{\emph{2015} (PID)} & \multicolumn{1}{c}{\emph{2015} (Ideo)} & \multicolumn{1}{c}{\emph{2015} (Combined)} & \multicolumn{1}{c}{\emph{2016} (PID)} & \multicolumn{1}{c}{\emph{2016} (Ideo)} & \multicolumn{1}{c}{\emph{2016} (Combined)} \\ 
\\[-1.8ex] & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)}\\ 
\hline \\[-1.8ex] 
 Age: 30-44 & 0.136^{**} & 0.080^{*} & 0.088^{**} & 0.062 & 0.007 & 0.020 \\ 
  & (0.034) & (0.033) & (0.033) & (0.035) & (0.033) & (0.034) \\ 
  Age: 45-59 & 0.205^{**} & 0.126^{**} & 0.136^{**} & 0.123^{**} & 0.068^{**} & 0.079^{**} \\ 
  & (0.037) & (0.035) & (0.035) & (0.029) & (0.026) & (0.027) \\ 
  Age: 60+ & 0.242^{**} & 0.149^{**} & 0.159^{**} & 0.203^{**} & 0.120^{**} & 0.139^{**} \\ 
  & (0.051) & (0.044) & (0.044) & (0.028) & (0.027) & (0.028) \\ 
  Race: Black & 0.010 & -0.044 & -0.020 & -0.047 & -0.056 & -0.031 \\ 
  & (0.047) & (0.043) & (0.044) & (0.046) & (0.052) & (0.050) \\ 
  Race: Hispanic & -0.017 & -0.063 & -0.061 & 0.110 & 0.121^{*} & 0.114 \\ 
  & (0.054) & (0.046) & (0.048) & (0.066) & (0.061) & (0.062) \\ 
  Race: White & -0.038 & -0.091^{**} & -0.085^{*} & 0.021 & 0.047 & 0.033 \\ 
  & (0.037) & (0.032) & (0.034) & (0.040) & (0.039) & (0.040) \\ 
  Female & -0.022 & -0.009 & -0.006 & -0.024 & -0.006 & -0.004 \\ 
  & (0.030) & (0.027) & (0.026) & (0.021) & (0.021) & (0.021) \\ 
  Income level & -0.002 & -0.002 & -0.002 & 0.005 & 0.005 & 0.004 \\ 
  & (0.005) & (0.004) & (0.004) & (0.003) & (0.003) & (0.003) \\ 
  High school & 0.019 & 0.038 & 0.035 & 0.079 & 0.006 & 0.034 \\ 
  & (0.071) & (0.067) & (0.070) & (0.073) & (0.061) & (0.066) \\ 
  Some college & -0.048 & -0.031 & -0.030 & 0.055 & -0.013 & 0.011 \\ 
  & (0.062) & (0.063) & (0.067) & (0.071) & (0.059) & (0.064) \\ 
  College graduate & -0.026 & -0.005 & -0.002 & 0.017 & -0.020 & -0.007 \\ 
  & (0.068) & (0.071) & (0.072) & (0.071) & (0.061) & (0.065) \\ 
  Postgraduate & -0.117 & -0.061 & -0.057 & -0.006 & -0.050 & -0.025 \\ 
  & (0.076) & (0.075) & (0.077) & (0.072) & (0.062) & (0.066) \\ 
  Democrat & -0.258^{**} &  & -0.106 & -0.189^{**} &  & -0.124^{*} \\ 
  & (0.060) &  & (0.059) & (0.044) &  & (0.050) \\ 
  Independent & -0.074 &  & -0.020 & -0.017 &  & -0.024 \\ 
  & (0.062) &  & (0.060) & (0.046) &  & (0.050) \\ 
  Republican & 0.040 &  & -0.013 & 0.127^{**} &  & 0.027 \\ 
  & (0.067) &  & (0.070) & (0.048) &  & (0.054) \\ 
  Conservative &  & 0.213^{**} & 0.214^{**} &  & 0.213^{**} & 0.183^{**} \\ 
  &  & (0.055) & (0.068) &  & (0.044) & (0.055) \\ 
  Liberal &  & -0.110^{*} & -0.053 &  & -0.127^{**} & -0.061 \\ 
  &  & (0.047) & (0.060) &  & (0.043) & (0.050) \\ 
  Moderate &  & -0.043 & -0.019 &  & 0.008 & 0.027 \\ 
  &  & (0.042) & (0.055) &  & (0.038) & (0.046) \\ 
  Very conservative &  & 0.303^{**} & 0.297^{**} &  & 0.308^{**} & 0.266^{**} \\ 
  &  & (0.057) & (0.063) &  & (0.059) & (0.065) \\ 
  Very liberal &  & -0.208^{**} & -0.144^{*} &  & -0.165^{**} & -0.099^{*} \\ 
  &  & (0.048) & (0.060) &  & (0.040) & (0.048) \\ 
  Constant & -0.093 & -0.144^{*} & -0.136^{*} & -0.283^{**} & -0.288^{**} & -0.276^{**} \\ 
  & (0.075) & (0.059) & (0.066) & (0.081) & (0.074) & (0.077) \\ 
  &  &  &  &  &  &  \\ 
Party ID & \checkmark &  & \checkmark & \checkmark &  & \checkmark \\ 
Ideology &  & \checkmark & \checkmark &  & \checkmark & \checkmark \\ 
N & \multicolumn{1}{c}{861} & \multicolumn{1}{c}{861} & \multicolumn{1}{c}{861} & \multicolumn{1}{c}{1,903} & \multicolumn{1}{c}{1,903} & \multicolumn{1}{c}{1,903} \\ 
Adjusted R$^{2}$ & \multicolumn{1}{c}{0.192} & \multicolumn{1}{c}{0.291} & \multicolumn{1}{c}{0.299} & \multicolumn{1}{c}{0.238} & \multicolumn{1}{c}{0.281} & \multicolumn{1}{c}{0.302} \\ 
\hline \\[-1.8ex] 
\multicolumn{7}{l}{$^{*}$p $<$ .05; $^{**}$p $<$ .01; $^{***}$p $<$ [.***]} \\ 
\multicolumn{7}{l}{OLS regressions with HC2 robust standard errors in parentheses; YouGov survey data with weights applied.} \\ 
\end{tabular} 
\end{table}

{[}TODO: Interpretation/explanation of big regression table.{]}

\begin{center}\includegraphics[width=1\linewidth,]{output/figures/figure_2} \end{center}

{[}Following Converse (1964), we stratify individuals into political
ideologues, near ideologues, and true moderates.{]}

{[}Hump for ``no issue content''\ldots{} a right-wing ``shy trump
voter'' affecting the low-information/education/cognitive ability
voters?{]}

\begin{center}\includegraphics[width=1\linewidth,]{output/figures/figure_3} \end{center}

{[}Cross-tables of class proportion.{]} {[}2015: Rep Ideologue Dem
Ideologue Rep Near Ideologue Dem Near Ideologue True Moderate 7.112
8.980 9.124 11.638 13.649 Moderate Partisan No issue content 18.032
31.466 {]} {[}2016: Rep Ideologue Dem Ideologue Rep Near Ideologue True
Moderate Dem Near Ideologue 9.116 11.704 11.943 12.898 14.968 Moderate
Partisan No issue content 16.799 22.572 {]}

{[}ALL GIM Tests return same \(p\)-value, 0.03225806, and seemingly
arbitrary test statistic magnitudes, which may requite further
investigation.{]}

\hypertarget{url-section-name-proxies-perform-slightly-better-than-logistic-classification-of-news-alignment}{%
\subsection{URL Section Name Proxies Perform Slightly Better than
Logistic Classification of News
Alignment}\label{url-section-name-proxies-perform-slightly-better-than-logistic-classification-of-news-alignment}}

\begin{center}\includegraphics[width=0.75\linewidth,]{output/figures/figure_4} \end{center}

{[}Writeup overlap coefficients, interpretation, KS tests and
bootstrapped KS tests manually.{]}

\begin{table}[H] \centering 
  \caption{\textbf{Determinants of Media Diet Slant (Using URL Section Proxies)}} 
  \label{} 
\small 
\begin{tabular}{@{\extracolsep{0pt}}lD{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} } 
\\[-1.8ex]\hline \\[-1.8ex] 
\\[-1.8ex] & \multicolumn{5}{c}{Average media diet slant (news/politics only)} \\ 
 & \multicolumn{1}{c}{(PID)} & \multicolumn{1}{c}{(Ideo)} & \multicolumn{1}{c}{(Combined)} & \multicolumn{1}{c}{(Interest)} & \multicolumn{1}{c}{(Party x Interest)} \\ 
\\[-1.8ex] & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} & \multicolumn{1}{c}{(5)}\\ 
\hline \\[-1.8ex] 
 Age: 30-44 & 0.101^{**} & 0.083^{*} & 0.090^{*} & 0.090^{*} & 0.068^{**} \\ 
  & (0.035) & (0.037) & (0.035) & (0.035) & (0.025) \\ 
  Age: 45-59 & 0.122^{**} & 0.082^{*} & 0.103^{**} & 0.104^{**} & 0.086^{**} \\ 
  & (0.034) & (0.036) & (0.035) & (0.036) & (0.025) \\ 
  Age: 60+ & 0.177^{**} & 0.149^{**} & 0.161^{**} & 0.162^{**} & 0.144^{**} \\ 
  & (0.035) & (0.037) & (0.036) & (0.037) & (0.025) \\ 
  Race: White & 0.011 & 0.038 & 0.017 & 0.016 & 0.010 \\ 
  & (0.038) & (0.038) & (0.038) & (0.038) & (0.032) \\ 
  Race: Black & 0.095^{*} & 0.093^{*} & 0.105^{*} & 0.105^{*} & 0.104^{**} \\ 
  & (0.040) & (0.045) & (0.041) & (0.041) & (0.038) \\ 
  Race: Hispanic & 0.053 & 0.059 & 0.057 & 0.056 & 0.047 \\ 
  & (0.054) & (0.059) & (0.054) & (0.054) & (0.043) \\ 
  Female & -0.043^{*} & -0.053^{*} & -0.043^{*} & -0.043^{*} & -0.040^{*} \\ 
  & (0.021) & (0.022) & (0.021) & (0.022) & (0.016) \\ 
  Income level & 0.002 & 0.002 & 0.002 & 0.002 & 0.002 \\ 
  & (0.004) & (0.004) & (0.004) & (0.004) & (0.003) \\ 
  High school & -0.129^{*} & -0.156^{*} & -0.142^{*} & -0.142^{*} & -0.130^{**} \\ 
  & (0.065) & (0.075) & (0.069) & (0.069) & (0.049) \\ 
  Some college & -0.149^{*} & -0.163^{*} & -0.159^{*} & -0.159^{*} & -0.148^{**} \\ 
  & (0.062) & (0.070) & (0.065) & (0.065) & (0.048) \\ 
  College graduate & -0.149^{*} & -0.167^{*} & -0.153^{*} & -0.153^{*} & -0.142^{**} \\ 
  & (0.064) & (0.074) & (0.067) & (0.067) & (0.050) \\ 
  Postgraduate & -0.188^{**} & -0.198^{**} & -0.184^{**} & -0.184^{**} & -0.181^{**} \\ 
  & (0.066) & (0.075) & (0.069) & (0.070) & (0.052) \\ 
  Democrat & -0.138^{**} &  & -0.101^{**} & -0.101^{**} & -0.199^{**} \\ 
  & (0.035) &  & (0.038) & (0.038) & (0.032) \\ 
  Republican & 0.116^{**} &  & 0.076 & 0.076 & 0.055 \\ 
  & (0.035) &  & (0.042) & (0.042) & (0.033) \\ 
  Conservative &  & 0.173^{**} & 0.098 & 0.098 & 0.059 \\ 
  &  & (0.041) & (0.051) & (0.051) & (0.052) \\ 
  Liberal &  & -0.078 & -0.020 & -0.020 & -0.035 \\ 
  &  & (0.040) & (0.043) & (0.043) & (0.054) \\ 
  Moderate &  & 0.047 & 0.047 & 0.047 & 0.025 \\ 
  &  & (0.039) & (0.040) & (0.041) & (0.050) \\ 
  Very conservative &  & 0.262^{**} & 0.163^{**} & 0.164^{**} & 0.117^{*} \\ 
  &  & (0.049) & (0.058) & (0.060) & (0.060) \\ 
  Very liberal &  & -0.120^{*} & -0.060 & -0.059 & -0.061 \\ 
  &  & (0.047) & (0.050) & (0.051) & (0.059) \\ 
  Political Interest &  &  &  & -0.002 & 0.140^{**} \\ 
  &  &  &  & (0.022) & (0.044) \\ 
  Democrat x Interest &  &  &  &  & 0.240^{**} \\ 
  &  &  &  &  & (0.049) \\ 
  Republican x Interest &  &  &  &  & 0.069 \\ 
  &  &  &  &  & (0.050) \\ 
  Constant & -0.060 & -0.124 & -0.099 & -0.098 & -0.150 \\ 
  & (0.079) & (0.084) & (0.083) & (0.084) & (0.078) \\ 
  &  &  &  &  &  \\ 
Party ID & \checkmark &  & \checkmark & \checkmark & \checkmark \\ 
Ideology &  & \checkmark & \checkmark & \checkmark & \checkmark \\ 
Political Interest &  &  &  & \checkmark & \checkmark \\ 
N & \multicolumn{1}{c}{920} & \multicolumn{1}{c}{924} & \multicolumn{1}{c}{920} & \multicolumn{1}{c}{920} & \multicolumn{1}{c}{920} \\ 
Adjusted R$^{2}$ & \multicolumn{1}{c}{0.245} & \multicolumn{1}{c}{0.220} & \multicolumn{1}{c}{0.265} & \multicolumn{1}{c}{0.264} & \multicolumn{1}{c}{0.290} \\ 
\hline \\[-1.8ex] 
\multicolumn{6}{l}{$^{*}$p $<$ .05; $^{**}$p $<$ .01; $^{***}$p $<$ [.***]} \\ 
\multicolumn{6}{l}{OLS regressions with HC2 robust standard errors in parentheses; YouGov survey data with weights applied.} \\ 
\end{tabular} 
\end{table}

\begin{center}\includegraphics[width=1\linewidth,]{output/figures/figure_5} \end{center}

{[}TODO: Results do not replicate those of Guess (2021). Either
classification strategies are measuring fundamentally different concepts
or there is so much heterogeneity in individual news preferences that
different samples generate significantly different results, and thus
future studies of individual need extremely large \(n\) (i.e., perhaps
around 10,000 or more randomly sampled respondents).{]}

\hypertarget{lets-get-loco-whats-driving-these-disparities-in-results}{%
\subsection{Let's Get LOCO: What's Driving These Disparities in
Results?}\label{lets-get-loco-whats-driving-these-disparities-in-results}}

\begin{center}\includegraphics[width=1\linewidth,]{output/figures/figure_6} \end{center}

{[}LOCO: Leave-One-Covariate-Out Inference{]}

\hypertarget{discussion-and-conclusion}{%
\section{Discussion and Conclusion}\label{discussion-and-conclusion}}

\bibliographystyle{unsrt}
\bibliography{references.bib}


\end{document}
